The impact of consumer health information on the demand for health services

The Quarterly Review of Economics and Finance 53 (2013) 1–11

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The impact of consumer health information on the demand for health services夽 Debra Sabatini Dwyer a,∗ , Hong Liu b a b

Stony Brook University, Department of Health Care Policy and Management, Stony Brook, NY, 11793-8204, USA Central University of Finance and Economics, China Economics and Management Academy, Beijing, 100081, China

a r t i c l e

i n f o

Article history: Received 20 July 2011 Received in revised form 29 September 2012 Accepted 30 November 2012 Available online 31 December 2012 Keywords: Consumer health information Health care demand Patient trust

a b s t r a c t This paper empirically examines whether consumers use health information, from non-physician information sources, as a substitute or complement for health services – namely for physician visits and emergency room (ER) visits. An indicator of patient trust in physicians is developed and used as a proxy for potential unobserved heterogeneity that may drive both consumers’ propensity to seek information and to use physician services. The results, after correcting for sample selection bias and controlling for unobserved heterogeneity, concur with the literature, that consumer health information increases the likelihood of visiting a physician as well as the frequency of visits on average. However, low-trust consumers tend to substitute self-care through consumer health information for physician services. Further, better-informed consumers make significantly fewer ER visits suggesting that information may be improving efficiency in the market. © 2013 Published by Elsevier B.V. on behalf of The Board of Trustees of the University of Illinois.

JEL classification: I1 D12 D8

1. Introduction Uncertainty as to the quality of the product is perhaps more intense here than in any other important commodity. . . Because medical knowledge is so complicated, the information possessed by the physician as to the consequences and possibilities of treatment is necessarily very much greater than that of the patient, or at least so it is believed by both parties. Further, both parties are aware of this informational inequality, and their relation is colored by this knowledge. [Arrow, 1963, p. 951] Imperfect information has been cited as the key source of market failure in the health care sector, by Arrow (1963) and others (Hurley, 2000; Kenkel, 1990). In this passage Arrow introduces the notion of asymmetric information and its impact on the “doctor–patient” relationship where doctors must serve as agents for their less informed patients. And implicit in this arrangement is some degree of trust that must occur between physicians and their principles – namely their patients. The importance of the doctor–patient relationship is well documented as an important attribute of consumer choice and how health care resources are allocated. Holding all else constant, this model of

resource allocation inherently places much of the decision-making power on the physician, so that efficiency evaluation comes from understanding the objectives driving physician behavior. In the principal–agent framework, physicians incorporate the utility of their patients in their own profit maximizing objective function (McGuire, 2000). These two objectives in a physician’s model of behavior may at times offset each other depending on market incentives and payment systems so that the efficiency of final outcomes is unclear from a social welfare perspective. Recent market trends have allowed for a considerable shift in decision-making power away from the physician. One structural change is the exponential growth in the availability of information at lower cost regarding health care quality and treatment options. Another important change is in the health care delivery payment system itself with the movement toward managed care. How these structural changes impact patient behavior has not been established to our knowledge. The purpose of this paper is to evaluate the impact of this influx of health care information on demand for health care services, and how that impact may vary by health care delivery systems. 2. Background and literature review

夽 The authors acknowledge financial support from the Natural Science Foundation of China (Grant No. 71203244). ∗ Corresponding author. Tel.: +1 631 638 1009; fax: +1 631 444 6474. E-mail addresses: [email protected] (D.S. Dwyer), [email protected] (H. Liu).

The information boom, promoted by the rapid growth of the Internet market, provides a relatively inexpensive and easily updated way for consumers to seek health information apart from their physicians. According to Cline and Haynes (2001), more than 50 million people seek health-related information online, and that

1062-9769/$ – see front matter © 2013 Published by Elsevier B.V. on behalf of The Board of Trustees of the University of Illinois. http://dx.doi.org/10.1016/j.qref.2012.11.002

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number is growing. Information exchange on the efficacy and safety of drugs or treatments is common (Haas-Wilson, 2001). Popular media hosting public health discussions communicate an increasing breadth of health information to the masses. Patients are becoming more educated consumers of health production and medical care services. In functioning capitalist markets, consumers are able to make rational choices over the quantities of goods and services given their propensity to rank alternative bundles of goods and services. This has never been the case for health care which is peculiar in many ways. With information becoming more accessible, the patient’s propensity to rank goods and therefore behave as rational consumers may have increased (Thiede, 2005). Studies focusing on quality of care find that patients who take advantage of information regarding the quality of providers (e.g. provider quality report cards) are more likely to respond to that information by taking on a more active role in searching for hospitals or physicians (Cutler, Huckman, & Landrum, 2004; Dafny & Dranove, 2008; Harris, 2003). While they are choosing the higher quality providers (Dranove & Sfekas, 2008; Fanjiang, von Glahn, Chang, Rogers, & Safran, 2007; Mukamel & Mushlin, 1998), they tend to also have worse health outcome in the short run (Dranove, Kessler, McClellan, & Satterthwaite, 2003). In this paper, we focus on quantity of care. Both are important given policy objectives. There is growing literature on the impact of the proliferation of health information on health service utilization. The findings to date suggest a dominating complementary relationship between different sources of information (Dranove et al., 2003; Hsieh & Lin, 1997; Kenkel, 1990; Lee, 2008; Parente, Salkever, & Davanzo, 2005; Suziedelyte, 2012). In other words, patients, who have more health information to improve health, are also more likely to see a physician in the formal medical services market. This is expected given there is selection on health status and health related preferences into the market for health promoting products. Theoretically it follows that the search behavior of consumers for information would vary by their various expected costs and benefits from the search (Stigler, 1961); which will depend on their preferences, cognitive ability, perceived self-efficacy, health status, time constraints, opportunity costs, income and other factors (Wagner, Hibbard, Greenlick, & Kunkel, 2001a). If consumer health information can serve to substitute time with physician to obtain this information, we might see a decline in the demand for office visits/utilization. Studies on a community-wide information intervention (Healthwise Communities Project [HCP]) find evidence of a substitution effect of information in that self-care health information is associated with decreased in phone calls to physicians for advice (Wagner, Hu, & Hibbard, 2001b) and pediatric utilization (Wagner & Greenlick, 2001). However, the quasi-experimental design of the HCP intervention raises concerns about bias and the generalizations of their results. Appropriate substitution of provider time spent providing information available at lower cost would ease the burden of excess demand in the health care sector and could solve some of the issues associated with spiraling costs and inadequate access to scarce medical resources. This is all true in theory if some conditions are met. A more complete theory would account for the intangibles of the doctor–patient relationship that may not be replaced with information – particularly if it is necessary to convert information to knowledge. Knowledge from information is non-marketable. Arrow (1963) predicts market failures and subsequent inefficient outcomes as a consequence of information limitations. And even with investments in information, conversion to knowledge is not a certainty. Even with all the relevant medical information available, patients may have limited abilities to utilize it in such a way as to efficiently carry out medical decisions independent of their

physicians. So the magnitude of the effect of information on the market is unclear and this becomes an empirical question. Clearly the shrinking information gap between consumers and physicians does change, to some extent, their relationship and therefore the functioning and consequential resource allocations within the market. Data limitations contribute to a shortage of empirical work on health information search behavior. Pauly and Satterthwaite (1981) were among the first to incorporate the availability of information into a model of health demand – proxying for consumer health information with physician density in an area. It is a less relevant proxy for information in the current managed care environment where patients have limited choice restricted to physicians in the provider network. Based on work by Kenkel (1990), researchers have widely adopted a direct measure of consumer health knowledge constructed from the correct responses to survey questions that translate into indicators of consumer health knowledge which they then use to examine information on health behaviors (Hsieh & Lin, 1997; Kenkel, 1990; Parente et al., 2005). Knowledge regarding a particular health issue is not necessarily a valid indicator of investments in information – although there would be a correlation. Knowledge can come from a variety of sources and is state dependent. While knowledge represents a propensity to digest information and should impact outcomes, it is not the focus of this study. The more recent work by Suziedelyte (2012) and Lee (2008) do use Internet informationsearching impact on utilization and our work will build on this literature. In this paper, we seek to address the impact of investments in information outside of the doctor–patient experience (including but not limited to the Internet) on health care utilization. It is ambiguous in theory whether information is serving as a complement or substitute for physician expertise. While one might expect that information would complement medical service use among those who are in the market for health improvements, it is possible that the commodities serve as substitutes if what can be learned in the provider–patient encounter is available elsewhere. Given our propensity to be risk-averse with our health, we would err on the side of seeing a doctor when we are unsure. Information availability may help us learn whether we need to go to the physician or not, or may substitute a follow-up visit for clarification on our diagnoses, particularly if we are not comfortable with the provider’s recommendations. We examine both routine care and acute care by measuring office visits and emergency room (ER) visits to assess the potential for efficiency improvements. Information available through the Internet, or from other non-physician sources, may have a bigger impact on the decision to visit an ER given that often happens after hours when physicians are less accessible. Given the high time cost and disutility associated with visiting an ER, patients are more prone to choose that source of care when none other are available and they have no alternative source for determining whether they need to be seen. The paper is structured as follows. The conceptual framework and hypotheses to be tested are presented in Section 3. Section 4 discusses the econometric concerns and corresponding empirical methodologies. In Section 5, we describe the dataset and data construction. The results of the analysis are reported in Section 6 followed by our conclusions, policy inferences, and future directions. 3. Conceptual framework The conceptual framework builds on the work of Grossman’s (1972) human capital model. Consumers maximize utility over health and other goods subject to budget and production

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constraints. Individuals gain utility directly from being healthy, or indirectly from the greater efficiency of both consumption and leisure that is associated with better health. Health is a stock that initially depends on genetics and latterly can be produced using health inputs. However, markets for medical care are characterized by various sorts of uncertainty, for example, patients’ uncertainty about the health conditions, uncertainty about effectiveness of medical treatments, and uncertainty about choices of medical care alternatives (Arrow, 1963; Haas-Wilson, 2001). The uncertainty in medical care markets makes health information a valuable commodity to consumers since it allows them to make better decisions about medical care (Arrow, 1963; Kenkel, 1990). It is also a costly commodity which takes time and money to search. The literature states that there are at least three types of health information useful to consumers: diagnostic information, treatment information and physician-specific quality information (Haas-Wilson, 2001). As a derived demand from the demand for health, consumers’ search behaviors for information vary by their expected costs and benefits of the information acquired (Arrow, 1963; Stigler, 1961). Historically, with limited health information, patients mainly relied on the doctor–patient relationship and medical professionals to improve their health so that the subjective choices faced by the individual patient were limited in the process of producing a desired level of health. With the development of new sources of information, especially the Internet, patients become betterinformed consumers of medical care. Information expands the consumers’ choice sets so that the consumers are more likely to make better decisions regarding medical services demand autonomously (Frey & Foppa, 1986; Lee, 2008; Thiede, 2005). Consumers now face two choices – whether to search for some information regarding health production on their own, or to completely rely on the medical market. The information search behavior generally precedes their medical care utilization decisions (Thiede, 2005). Despite access to information regarding alternative treatments independent of their physicians, consumers still make medical care investment choices within the principal–agent framework of health care, where physicians serve as agents for their principal – the consumers (McGuire, 2000). The intangibles of the doctor–patient relationship may never be replaced with information from the Internet (Haas-Wilson, 2001). As a key indicator of the quality of this agency relationship, patient trust provides a metric as to how dependent the principal (the patient) is on her physician agent (Dwyer, Liu, & Rizzo, 2012), which works as a taste shifter in the health production function. In a Bayesian-updating framework, people start with prior perceptions about physician agency, and update it as they obtain additional information from the experience (Sloan, 2001). At any given point in time, prior trust in physician services significantly motivates consumer choice (Kolstad & Chernew, 2009; Thiede, 2005). The impact of health information on the optimal input of formal medical care is complex. First, information has expanded those choices to include self-care through health information, formal medical care, or some combination. In any case, at the margin we may see a decline in the use of the formal medical sector given the propensity to substitute self-care for the production of health (Lee, 2008). It is more likely to occur in the setting of a low-trust doctor–patient relationship, where perceived health gain for the medical service input is low. An example for this effect is that the informed patient may reduce health risks or improve health outcomes through self-diagnosis, early detection and self-treatment. Consumers with a greater distaste for medical services or facing higher opportunity costs of seeking medical services, either due to a lack of health insurance, or greater time costs associated with the

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visits, are more likely to choose information substitutes (Bundorf, Baker, Singer, & Wagner, 2004). Second, as Kenkel (1990) stated, poorly informed consumers tend to underestimate the marginal product of medical care. Access to health information prior to their purchase of physician services allows consumers to choose high-quality physicians (Dranove & Sfekas, 2008; Fanjiang et al., 2007; Mukamel & Mushlin, 1998), and also allows them to interact productively with their physicians (Murray et al., 2003). In this case, consumer health information is more likely to act as a complement to medical care use (Hsieh & Lin, 1997; Kenkel, 1990; Parente et al., 2005). However, given the consumer has made the decision to visit the physician, the decision how much medical care to purchase is also related to the extent of consumer’s health information and physician’s agency behavior (Kenkel, 1990). Due to information asymmetry inherent in the doctor–patient relationship, physicians may induce unnecessary demand that the costs outweigh the medical benefits. It is predicted that more informed consumers are subject to less induced demand, especially when they have appropriate skepticism about physician’s agency behavior (Davies & Rundall, 2000; Kenkel, 1990; Mechanic, 1998). In the era of managed care, physician induced demand may be less of a concern, but may still exist and at the margin should decline in the information boom era. Third, despite increased access to non-physician health information, patients may still lack the ability of turning information to medical knowledge, especially with the development of new medical technology (Suziedelyte, 2012). Because of the uncertainties and errors underlying non-physician health information, increasing access to information may make individuals more aware of their health and well-being, and reply more on physicians to make use of the information. As a result, consumer health information may have a positive effect on medical care use in the short term, but may decrease long-term health care utilization and costs. In sum, theoretically we hypothesize that consumer health information has an ambiguous effect, both substitution and complement, on medical care utilization. Among patients with prior low trust in physicians, the complement effect of non-physician health information may be smaller or there may be a net substitution effect. We thus also hypothesize that holding all else constant, the sign of the effect of health information will be conditional on patient prior trust in physicians. Without controlling for patient trust, the effect of information may be noisy. Because there will always be some degree of information asymmetry in the health care sector, there will always be some demand for medical services among those in the market for improving health. Any substitution that exists is likely to have a greater impact on the quantity of medical services rather than whether or not to use medical services. We therefore hypothesize that the impact of information will be greater for quantity of medical services rather than the decision to be in the market for medical services.

4. Data and variables 4.1. Data The data for this study are mainly from the Community Tracking Study (CTS) 2000–2001 Household Surveys, designed by the center for Studying Health System Change. This survey provides nationally representative data of health system change and its effects on the US non-institutionalized populations. Sixty communities were randomly selected across the nation using stratified sampling with probability in proportion to population size to ensure representation of the US population. The majority of the respondents were randomly selected for telephone interview through the use of a

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random-digit-dialing sampling methodology, but families without telephones were represented in the sample as well. They were provided with cellular telephones for the interviews. The content of the survey is comprehensive, including detailed information on patient trust, health information seeking, use of medical services, chronic diseases, individual attitudes, satisfaction with doctor visits and their health plan, insurance coverage, health status, and sociodemographic characteristics. The CTS Household Survey has four rounds to date (1996–1997, 1998–1999, 2000–2001, and 2003), but only Round 3 asks respondents specific questions about health information seeking from non-physician sources. This study is mainly cross sectional. The full 2000–2001 household sample consists of 59,725 individuals, among whom 27,487 respondents were randomly selected from Round 2 and reinterviewed in Round 3. This repeated subsample is used as the data samples in this paper. We restrict the individual-level data sample to adults who are age 18 years and over. In addition, the 2001 Area Resource File (ARF) is linked to the CTS Household Survey by state and county to provide physicians’ characteristics at the market level, as well as communities’ characteristics. The summary statistics about the data sample, which is weighted to control for stratification, clustering and oversampling to represent the US national population, are presented in Table 1. 4.2. Variables 4.2.1. Dependent variables In this study, health service demand is mainly measured by physician visits and ER utilization. As for physician visits, two outcomes variables are constructed: a binary variable indicating any physician visit, and then positive numbers of physician visits during the previous 12 months. In our data sample, nearly 80% of respondents have visited physicians at least once in the past 12 months. ER utilization is measure by the number of ER visits in the past 12 months, which has a mean of about 0.3 times. 4.2.2. Main independent variables Our key independent variable is the indicator of consumer health information searching from non-physician sources. There are 7 questions in the survey asking, respectively, whether the respondent obtains health information from the Internet, TV or radio, books or magazines, friends, non-physician health care professionals, health care organizations and other sources. The answer is binary with 1 for “yes” and 0 for “no”. The first four are main information sources. We constructed the measure of consumer general health information searching using a binary indicator assigned a value of 1 if the respondent searches health information from any of the non-physician sources and a value of 0 if no search at all.1 As shown in Table 1, about 38% of respondents search from health information sources other than physicians. Another key independent variable is prior patient trust, which helps identify the substitution effect of non-physician health information. There are four questions about individuals’ trust in their physicians in CTS. Respondents are asked to think about their usual or last physician visit and indicate whether they agree with the following four statements: (1) “I think my doctor may not refer me to

1 Exploratory factor analysis on information indicators yields one significant common factor. The constructed information measure is found to be highly correlated with the underlying factor constructed from the data reduction technique, with a correlation coefficient 0.9472. We also used a continuous variable indicating number of total non-physician information sources consumer uses, which is the sum of the answers to all 7 survey questions, reflecting the level of information searching efforts. The results are robust to different specifications of the information measurement; we only report the results using binary information measure here.

a specialist when needed”; (2) “I trust my doctor to put my medical needs above all other considerations when treating medical problems”; (3) “I think my doctor is influenced by health insurance company rules when making decision about my medical care”; (4) “I sometimes think that my doctor might perform unnecessary tests or procedures”. Each of the four statements has a response in a 5-point Likert format from “strongly agree” to “strongly disagree”. We reversely coded the second item, so that higher scores indicated greater trust for each question. We then construct a dichotomous variable indicating whether the patient has low trust (or distrust) in physician,2 which is 1 if any response to the above four statements is not 5, and 0 otherwise. About 30% of respondents fully trust their physicians.3 As specified by McGuire (1983), patients accumulate their trust stock in physicians through their medical experience. Therefore, prior patient trust is used here, obtained from CTS Round 2.4 4.2.3. Other independent variables Our empirical model also controls for other covariates affecting individuals’ demand for medical services. The individual-level control variables include health status (overall health status, ADL, mental, physical, detailed chronic diseases), health insurance status (private, HMO, uninsured, Medicare, Medicaid), race or ethnicity (white, African American, Hispanic, Asian), household income, hourly wages, individual preference and other demographic variables. There are two variables measuring individual preferences surrounding medical products. One asks respondents whether they would be willing to accept limited provider choice in order to save money on out-of-pocket expense for health care (limited choice). The other asks about whether the respondent agrees with the statement that he or she is more likely to take risks than the average person. From the merged Area Resources File, we are able to control for physician characteristics at the community level, including physician density (physician per population), percentage of female physicians, percentage of physicians who are board certified, and population density. As discussed, the exclusion restriction for the propensity to use any physician services is whether an individual opts to be uninsured. To address the endogeneity of consumer information seeking, we use proportion of female-headed family in the community and three education dummies (high school, college and graduate) as instrumental variables. 5. Empirical modeling We aim to model whether consumers use health information, from non-physician information sources, as a substitute or complement for health services, and given its existence, the average magnitude of the effect. In theory, and even empirically, we believe the two investments in health are likely to be complements to the health production process. It is possible for them to be complements, with some degree of substitutability. In theory a substitution effect is likely to exist, but has been difficult to uncover

2 The overall composition of the trust scale in CTS is not an exact match to the validated trust scales proposed in the literature. Those scales have more dimensions, but the subset of questionnaire items found in the CTS trust scale are virtually identical to questionnaire items used in these other trust scales (Doescher, Saver, Franks, & Fiscella; Pearson & Raeke, 2000). 3 Such high levels of patient trust are consistent with a previous study by Hesse et al. (2005). They found that patients trust their physicians a lot to provide them accurate medical information about cancer, but they are also likely to first use the Internet when seeking health information. 4 To test the robustness of our results, we also separate out the measures of trust in the regressions. The results (not reported here) are very similar to the main results reported in the paper.

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Table 1 Summary statistics. Variable

Obs

Mean

S.E.

Min

Max

Dependent variables Any physician visit Num. of physician visits Num. of emergency room visits

23,486 19,255 23,486

0.807 4.872 0.315

0.004 0.053 0.009

0 1 0

1 30 7

Main independent variables Any info. search Getinf1 (Internet) Getinf2 (friends) Getinf3 (TV/radio) Getinf4 (books/magazines) Getinf5 (other sources) Getinf6 (health care professional)a Getinf7 (health care organization) Low prior patient trust

22,558 22,558 22,558 22,558 22,558 22,558 22,558 22,558 18,980

0.375 0.160 0.183 0.107 0.235 0.005 0.008 0.007 0.721

0.005 0.004 0.004 0.003 0.004 0.000 0.001 0.001 0.007

0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1

Other explanatory variables Health status Poor health ADL Physical limited work Emotional limited work Diabetes Arthritis Asthma Hypertension Heart disease Skin cancer Other cancers Depression

23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486

0.158 0.395 0.234 0.107 0.095 0.249 0.087 0.254 0.065 0.057 0.048 0.104

0.005 0.007 0.003 0.003 0.003 0.006 0.002 0.004 0.002 0.003 0.002 0.004

0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1

Health insurance type HMO Uninsured Medicaid Medicare

23,486 23,486 23,486 23,486

0.388 0.083 0.038 0.239

0.010 0.004 0.003 0.006

0 0 0 0

1 1 1 1

Individual preference Preference: somewhat risk-taking Preference: strongly risk-taking Preference: limited choice

22,410 22,410 22,178

0.269 0.142 0.537

0.004 0.002 0.007

0 0 0

1 1 1

Demographic characteristics Age Male Black Hispanic Asian Married Any kid Large MSA Small MSA

23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486 23,486

49.319 0.467 0.081 0.074 0.033 0.685 0.393 0.703 0.067

0.219 0.002 0.010 0.013 0.003 0.006 0.007 0.027 0.012

18 0 0 0 0 0 0 0 0

91 1 1 1 1 1 1 1 1

Income Family income

23,486

5.708

0.068

0

15

Time cost Log wage

23,486

1.622

0.018

0

3.912

Community level variables Population per mile2 (per 1000) Physician per pop. % Female physicians % Board

23,486 23,486 22,566 22,566

1.041 0.270 0.222 0.332

0.110 0.009 0.012 0.015

1 0 0 0

45.95 2.507 1 1

Exclusive restriction for entry into physician services 23,486 Opt to be uninsured

0.008

0.001

0

1

Instrumental variables for consumer information search 23,486 Education: High School 23,486 Education: College 23,486 Education: Graduate 23,486 % Families w/female head

0.356 0.433 0.084 0.176

0.007 0.008 0.003 0.004

0 0 0 0.046

1 1 1 0.446

a

Here health care professionals as the 6th information source exclude physicians.

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empirically. According to our conceptual analysis showing the dual effect of consumer health information on medical care demand as conditional on patient trust in their physician, we include the interaction of consumer information and patient prior trust to allow for heterogeneous individuals. The main equation is: M = ˇ0 + ˇ1 Info + ˇ2 Info ∗ Ltrust + ˇ3 Ltrust + ˇ4 X + ε

(1)

where M denotes medical care demand, measured by physician visits and emergency room visits; Info indicates consumer health information searching behavior; Ltrust is a binary variable measuring the degree of patients’ distrust in physicians; X is a vector of exogenous observable individual characteristics and county-level physician characteristics; ε is the disturbance term. Demeaned Ltrust is used to interact with Info, so that the coefficient, ˇ2 , on the interaction can reflect the substitution effect of consumer health information on medical care use, while the coefficient ˇ1 on Info indicates the average effect. As per the conceptual discussion, we expect that ˇ1 > 0 and ˇ2 < 0. 5.1. Physician visits: a two-stage decision process The number of physician visits is not perfectly continuous in that it takes a limited number of values with a bunching at zero. This creates some empirical concern with running ordinary least squares on a left censored dependent variable. In addition, patients make a decision to see a doctor for the first visit, and then it is unclear who decides the number of visits after that. A doctor might take over as the agent of the patient and decide the medical needs of that patient, despite the degree of information the patient possesses. Our hypothesis is that even the number of visits will be influenced by informed patients, but consumers are still limited in treatment choices once they see an expert. Given this attribute of the market, the important consumer choice is over whether to see a physician or not. If we care about estimating the magnitude of potential savings from substituting toward lower cost information, we do need the number of visits. So we need to model both the decisions to participate in the health care services market, and the consequential quantity demanded in the market. We use the Heckman’s two-step approach. In estimating the equation for the positive number of physician visits, we include the inverse mills ratio (lambda) which comes from a probit regression for the decision to seek a physician using all observations. We use as an exclusion restriction the choice to “opt out” of an employer provided health insurance plan – so the indicator takes a one if an individual is eligible for employer provided health insurance but not covered, and 0 otherwise. We believe this reflects a choice or preference for participation in the health care services market, but not a significant determinant for frequency of physician visits.5 5.2. Potential endogeneity problem: consumer health information and utilization The propensity to use physician services is likely to be correlated with the choice to use potential substitutes for health care services. This is why controlling for sample selection is so important. The lambda might alleviate the endogeneity bias if the propensity to use medical services at all, which is what lambda will be picking up, drives both information seeking and the number of visits. However we cannot assume that controlling for sample selection eliminates

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We empirically test the validity of this exclusion restriction by adding this indicator in the regression for frequency of physician visits, and find that it is not a significant determinant. The results are available upon request.

the concern associated with estimating a causal model of health information on health demand. Specifically, endogeneity may arise because there might be some unobserved heterogeneity that drives both the investments in non healthcare services treatment (information) and physician services. Unobserved propensity toward good health, or marginal value of health, may increase consumers’ use of both health information and health services. It results in a biased positive effect of information without proper controls for valuation of health. Moreover, other unobserved preference parameters such as an individual’s risk preference and time preference may also affect both consumer health information-seeking as well as health care utilization in some ambiguous ways. For example, people who are risk-averse may seek both health services and consumer health information, or they may use more health services but be weary of consumer information. In sum, in models of entry into physician service and number of physician visits, Info may be endogenous and correlated with the residuals of both equations so that a sample selection correction for propensity to initiate a first visit is insufficient for correcting the potential bias. The direction of the potential bias is unclear according to our analysis. Finding an instrument to purge that propensity to invest from the indicator of information is challenging. We want to be able to identify the potential for information to substitute physician services, once the decision to invest has been made. We want to keep the component of information searching purely related to propensity to gain valuable knowledge from search but independent of the need to invest in health (health status and preferences for health). To control for this, Kenkel (1990) and Hsieh and Lin (1997) have used education as an instrument for health information. Education should impact one’s propensity to use information well – their propensity to gain from using information. But preferences for overall good health should theoretically not vary by education alone. Low educated people, holding all observable differences like income and health fixed, should have the same taste for healthy living as high educated people. While health and health insurance status might be correlated with education, thereby driving access to both information and physician services, they are observables we control for. Education does, however, directly affect access to information as well as expected gain from using it. After testing, we find that education has no independent effect on physician services beyond the observables correlated with it. So education predicts information but does not predict utilization of physician services, holding health, health insurance and income fixed. Education can be an exogenous instrument of information since it predicts demand for it without being correlated with the error term of the main equation. Also included as instrumental variables is a variable indicating proportion of female-headed families at the county level. As Pauly and Satterthwaite (1981) has noted, a female-headed family has a tighter time budget and fewer social networks, thus has a higher opportunity cost for searching health information. And it is uncorrelated with the residual in the main equations for health care demand. By using this instrumental variables approach we hope to capture the component of information that represents consumption of information independent of those unobservable preferences when observable health status, health insurance, income and demographic characteristics are controlled for.6

6 We also conduct the traditional 2SLS IV estimation with one endogenous variable, Info, and no interaction term for three outcome variables. It provides the empirical test for the validity of instrumental variables. The results (not reported here) show that the overidentifying restrictions are not rejected at any reasonable

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5.3. Econometric models

information and patient trust to make it consistent, as shown in Eq. (5).

5.3.1. Econometric model for physician visits In this section we combine the econometric concerns of Sections 5.1 and 5.2 with our model defined in the introduction part of Section 5. The decision over number of physician visits can be modeled as follows: Info = ˛0 + ˛1 X + ˛2 Z + ε0 ∗

(2)

ER = 0 + 1 Info + 2 Info ∗ Ltrust + 3 Ltrust + 4 X + ε3

(5)

where ER indicates the number of ER visits; ε3 is the disturbance term. To account for endogeneity, we estimate the parameters in Eq. (5), together with Eq. (2), using the Tobit instrumental variable estimator of Newey (1987).



PV = ı0 + ı1 Info + ı2 Info ∗ Ltrust + ı3 Ltrust + ı4 X + ı5 Z + ε1 (3) PV = 1 if PV > 0;

7

PV = 0 if otherwise.

{PVisits|PV = 1} = 0 + 1 Info + 2 Info ∗ Ltrust + 3 Ltrust + 4 X + ε2 (4) where PV* is the probability of visiting a physician; PVisits indicates the positive number of physician visits; Z are the instrumental variables for Info; Z denotes the choice to “opt out” of an employer provided health insurance plan, which is used as the exclusive restriction for the decision to see a doctor; ε0 , ε1 and ε2 are three disturbance terms which are independent of X and Z. We estimate Eqs. (2) and (3) using Amemiya’s (1979) Generalized Least Squares (AGLS) (Maddala, 1983, pp. 247–252; Newey, 1987), which is more efficient than the two-stage estimation. The estimator ı1 , the coefficient on Info, is exactly the average effect of information searching conditional on the exogenous variables, whereas the coefficient ı2 on the interaction term is predicted to show the substitution effect of information for the subgroup of people with prior low trust in physicians. To estimate the frequency of physician visits conditional on any use as shown in Eq. (4), we first estimate Eq. (2) from a probit of

 and then Info on X and Z and obtain the fitted probabilities Info,

 and Info  ∗ Ltrust as the instruestimate Eq. (4) by 2SLS using Info ments for Info and Info*Ltrust, respectively.7 If the sample selection bias for number of visits among users is significant, we estimate Eq. (4) in the presence of both endogeneity and selection. Following the estimation procedure suggested by Wooldridge (2002, p. 568), we add one more step after the estimation of Eq. (2). We estimate Eq. (3) using all observation with Info and Info*Ltrust replaced  and Info  ∗ Ltrust, and obtain the by the predicted probabilities Info inverse mills ratio, lambda. After that, Eq. (4) is estimated by 2SLS

 and Info  ∗ Ltrust as the instruments and lambda included with Info in the regressors. Similar to Eq. (3), the coefficient,  1 , indicates the average effect of consumer health information on frequency of physician visits, and the coefficient,  2 , is expected to show its substitution effect. 5.3.2. Econometric model for emergency room (ER) visits Unlike physician visits, going to an ER is a one-step decisionmaking process initiated by patients. We can use a Tobit model to address the censoring problem stemming from a limited number of response values with a great bunching at zero. Although the doctor–patient relationship may not play such an important role in the demand for ER services, given that patients initiate ER visits they deem emergent that often does not stem from interactions with a physician, we still keep the interaction between consumer

level for all three outcomes, and the weak identification test also shows that the IVs are not weak instruments. 7 When we have a binary endogenous explanatory variable, the fitted probabilities from a first-stage probit model is an “optimal instrument” for the endogenous variable (Wooldridge, 2002, p. 204).

6. Results 6.1. Propensity to seek health information from non-physician sources We begin by analyzing factors that predict one’s propensity to seek non-physician health information. If the factors that drive the propensity to search also drive health care demand, or in other words, respondents sort endogenously into health information seeking categories, this must be addressed in our econometric models of utilization. In Table 2 we see that lower prior patient trust significantly drives non-physician health information searching. This is consistent with our priors that the relationship between utilization of medical services and non-physician health information may in part be conditional on their prior trust in physicians. Other factors that influence the quest for health information are as expected. Education is positively and significantly associated with non-physician health information seeking as expected given a greater propensity to use the information well and gain from it. Female-headed families or families with children in general are significantly less likely to search for health information from non-physician sources. At the margin the opportunity cost of time is high for both female-headed families and families with kids. The likelihood of seeking non-physician health information increases with chronic diseases, HMO enrollment, family income, population density and physician density. Married people seek more information probably because couples at the margin have broader social networks of information sources as well as a stronger incentive to keep one’s partner healthy. Those less likely to seek non-physician health information include the elderly, males, and those enrolled in Medicare and Medicaid. Given that Medicare and Medicaid enrollees are usually old, poor, disabled and less educated, the result is not surprising. People reporting some preference for risk seek more health information than others, while those reporting strong risk-taking preferences are less likely to search although this result is not significant. 6.2. Health information and health services demand The empirical results for our model of the probability of visiting physicians are presented in Table 3. The simple probit results show that there exists a significant positive effect of health information searching on entry into physician service. Although insignificant, the negative sign on the interaction term between health information and prior low patient trust suggests that those who have relatively low trust in physicians may potentially work as their own health agents using non-physician health information as a substitute for physicians’ prescriptions. However, as discussed, the probit results of information seeking may be biased by the endogeneity problem. Using AGLS, we find that information seekers are 23.3% more likely to also see a doctor. People with prior low trust in physicians are 4% significantly less likely to visit physicians. There is no significant effect found for the interaction term between information seeking and patient trust. Comparison of the AGLS and probit results indicates that the

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D.S. Dwyer, H. Liu / The Quarterly Review of Economics and Finance 53 (2013) 1–11

Table 2 Regression results for consumers’ health information searching.

Table 3 The effect of consumer health information on entry into physician service. Probit Marginal effect

AGLS Marginal effect

Any Info search Prior low trust*Info Prior low trust Poor health ADL Phy. limited work Emo. limited work Diabetes Arthritis Asthma Hypertension Heart disease Skin cancer Other cancers Depression HMO Uninsured Medicaid Medicare Somewhat risk-taking Strongly risk-taking Limited choice Age Male Black Hispanic Asian Married Any kid Large MSA Small MSA Family income Log wage Pop. per mile2 Physician per pop. % Female physician % Board Opt to be uninsured

0.043(0.005)*** −0.015(0.012) −0.017(0.007)*** 0.008(0.009) 0.001(0.006) 0.045(0.006)*** −0.009(0.009) 0.050(0.007)*** 0.033(0.006)*** 0.028(0.008)*** 0.073(0.005)*** 0.074(0.008)*** 0.056(0.009)*** 0.048(0.010)*** 0.046(0.007)*** 0.013(0.005)*** −0.152(0.016)*** 0.025(0.014)* 0.007(0.009) −0.016(0.006)*** −0.037(0.008)*** −0.019(0.005)*** 0.000 (0.000) −0.086(0.005)*** −0.006(0.009) −0.031(0.011)*** −0.048(0.016)*** 0.013(0.006)** −0.016(0.006)*** 0.011(0.008) 0.019(0.013) 0.003(0.001)*** 0.003(0.002) −0.001(0.001) 0.044(0.015)*** 0.005(0.017) 0.013(0.013) −0.070(0.034)**

0.233(0.031)*** 0.019(0.050) −0.039(0.019)** 0.010 (0.009) −0.004(0.006) 0.024(0.008)*** −0.036(0.012)*** 0.043(0.008)*** 0.027(0.007)*** 0.022(0.008)** 0.069(0.006)*** 0.071(0.008)*** 0.046(0.010)*** 0.040 (0.012)*** 0.026(0.009)*** 0.012(0.005)** −0.143(0.017)*** 0. 038(0.013)** 0.016(0.010)* −0.023(0.006)*** −0.032(0.008)*** −0.016(0.005)*** 0.000(0.000) −0.062(0.007)*** −0.001(0.009) −0.024(0.011)** −0.050(0.017)*** 0.005(0.007) −0.008(0.006) 0.007(0.008) 0.017(0.014) 0.0014(0.0008)* 0.000(0.002) −0.001(0.001) 0.020(0.016) 0.000(0.018) 0.011(0.013) −0.047(0.034)

Pseudo R2 Number of Observations

0.1337 17,397

0.1326 17,397

Probit Coef. Low prior patient trust Poor health ADL Physical limited work Emotional limited work Diabetes Arthritis Asthma Hypertension Heart disease Skin cancer Other cancers Depression HMO Uninsured Medicaid Medicare Somewhat risk-taking Strongly risk-taking Limited choice Age Male Black Hispanic Asian Married Any kid Large MSA Small MSA Family income Log wage Pop. per mile2 Physician per pop. % Female physician % Board % Female-headed families High school College Graduate cons

0.141(0.022)*** 0.029(0.033) 0.107(0.025)*** 0.268(0.029)*** 0.297(0.035)*** 0.118(0.035)*** 0.114(0.026)*** 0.062(0.034)* 0.074(0.025)*** 0.054(0.043) 0.170(0.043)*** 0.131(0.046)*** 0.269(0.033)*** 0.031(0.021) −0.010(0.047) −0.144(0.060)** −0.107(0.037)*** 0.074(0.023)*** −0.029(0.031) −0.030(0.021) −0.003(0.001)*** −0.269(0.021)*** 0.005(0.038) 0.002(0.044) −0.005(0.058) 0.132(0.025)*** −0.103(0.025)*** 0.047(0.034) 0.058(0.061) 0.013(0.003)*** 0.012(0.009) 0.009(0.004)** 0.238(0.065)*** 0.036(0.074) 0.038(0.055) −0.488(0.224)** 0.193(0.041)*** 0.510(0.041)*** 0.707(0.048)*** −0.821(0.088)***

(Pseudo) R2 Observation

0.062 17,397

Marginal effect 0.055 0.011 0.042 0.105 0.117 0.046 0.044 0.024 0.029 0.021 0.067 0.052 0.106 0.012 −0.004 −0.055 −0.041 0.029 −0.011 −0.011 −0.001 −0.104 0.002 0.001 −0.002 0.051 −0.040 0.018 0.023 0.005 0.005 0.004 0.092 0.014 0.015 −0.002 0.075 0.197 0.276

Standard errors are in parentheses. * Significance at 10% confidence level. ** Significance at 5% confidence level. *** Significance at the 1% confidence level.

probit tends to yield a downward-biased estimate on the effect of information seeking on entry into physician service. Table 4 presents the empirical results for the model of frequency of physician visits. The OLS estimates in the first column suggest a significant positive association between information seeking and physician visits. Using Heckman’s two-step sequential health care demand process, we find the inverse mills ratio is significant at the 5% level in the visit frequency equation, which supports the sample selection bias model of conditional demand for physician visits. The exclusion restriction in the first equation, the “opt-out” decision, does significantly predict the decision to see a physician (Table 3). Specifically, the OLS estimate of health information is slightly biased upward by the sample selection. The IV results in Table 4 further address the potential endogeneity problem with IV estimation, as shown in the third column. Results in column four of Table 4 come from our preferred model which also controls for selection given it has proven to be significant in the second column, but becomes less so once we account for endogenous searching. This suggests that there is a relationship

Marginal effects are reported and standard errors are in parentheses. * Significance at 10% confidence level. ** Significance at 5% confidence level. *** Significance at the 1% confidence level.

between the propensity to see a physician and the propensity to seek information, and that in this model, information seeking is a more important predictor of utilization. Once we account for unobserved factors that drive both information search and utilization, and for patient trust in health care, the impact of information search becomes larger. In other words, interpretation of the role of information searching, holding trust and propensity to use constant is that information generates more care. Health information yields positive utility and more is better, whether it is derived from formal or informal care. Patients who distrust health care and have access to outside information use fewer services. This suggests there is a substitution effect for utilization among patients less tied to the formal health sector, which is seldom found in previous empirical studies.8

8 As shown in Tables 3–5 in the paper, patients willing to accept limited provider choice to save money on out-of-pocket expense for health care are less likely to utilize health services. It implies that “limited choice” may reflect individual taste for less health services given everything else the same. Consistently, when we interact this variable with health information, the results (not reported here, but available upon request) show that consumer health information has a positive effect on health

D.S. Dwyer, H. Liu / The Quarterly Review of Economics and Finance 53 (2013) 1–11

9

Table 4 The effect of consumer health information on num. of physician visits. OLS Coef.

Heckman Coef.

IV estimation (1) Coef.

IV estimation (2) Coef.

Any Info search Prior low trust*Info Prior low trust Poor health ADL Phy. limited work Emo. limited work Diabetes Arthritis Asthma Hypertension Heart disease Skin cancer Other cancers Depression HMO Uninsured Medicaid Medicare Somewhat risk-taking Strongly risk-taking Limited choice Age Male Black Hispanic Asian Married Any kid Large MSA Small MSA Family income Log wage Pop. per mile2 Physician per pop. % Female physician % Board Lambda cons

0.721(0.081)*** −0.244(0.173) −0.164(0.113) 1.659(0.126)*** 0.591(0.098)*** 1.903(0.110)*** 0.310(0.138)** 1.080(0.131)*** 0.463(0.099)*** 0.679(0.132)*** 0.652(0.094)*** 0.876(0.160)*** 0.821(0.164)*** 1.749(0.172)*** 1.108(0.128)*** 0.047(0.084) −1.237(0.208)*** 0.703(0.227)*** 0.135(0.144) −0.107(0.092) 0.235(0.123)* −0.528(0.081)*** −0.039(0.004)*** −0.478(0.085)*** −0.281(0.146)* −0.287(0.176)* −0.446(0.236)* 0.030(0.097) −0.011(0.099) 0.295(0.134)** 0.070(0.241) 0.018(0.011) −0.218(0.036)*** 0.042(0.015)*** −0.572(0.245)** 0.559(0.291)* −0.554(0.216)*** −1.504(0.748)** 5.448(0.283)***

0.617(0.096)*** −0.242(0.175) −0.113(0.116) 1.653(0.127)*** 0.581(0.099)*** 1.808(0.121)*** 0.323(0.140)** 0.977(0.142)*** 0.399(0.105)*** 0.622(0.136)*** 0.487(0.126)*** 0.739(0.175)*** 0.708(0.175)*** 1.667(0.178)*** 1.010(0.138)*** 0.015(0.086) −0.816(0.296)*** 0.650(0.231)*** 0.136(0.145) −0.069(0.094) 0.325(0.132)** −0.482(0.085)*** −0.040(0.004)*** −0.276(0.133)** −0.272(0.147)* −0.207(0.181) −0.342(0.243) −0.005(0.099) 0.024(0.101) 0.268(0.136)** 0.021(0.244) 0.010(0.012) −0.224(0.036)*** 0.044(0.016)*** −0.675(0.253)*** 0.536(0.293)* −0.585(0.218)*** −0.934(0.822) 6.053(0.414)***

3.168(0.566)*** −2.265(0.711)*** 0.540(0.303)** 1.659(0.130)*** 0.518(0.103)*** 1.649(0.129)*** 0.064(0.156) 0.993(0.138)*** 0.377(0.104)*** 0.590(0.138)*** 0.626(0.098)*** 0.849(0.165)*** 0.624(0.176)*** 1.620(0.180)*** 0.861(0.144)*** 0.051(0.087) −1.214(0.216)*** 0.903(0.239)*** 0.255(0.151)* −0.184(0.097)* 0.247(0.128)* −0.470(0.085)*** −0.036(0.004)*** −0.285(0.101)*** −0.243(0.152) −0.212(0.183) −0.478(0.244)** −0.070(0.102) 0.046(0.104) 0.254(0.140)* 0.026(0.250) −0.006(0.013) −0.250(0.038)*** 0.032(0.016)** −0.784(0.259)*** 0.520(0.301)* −0.595(0.223)***

2.793(0.680)*** −2.413(0.708)*** 0.648(0.311)** 1.653(0.129)*** 0.522(0.102)*** 1.623(0.129)*** 0.108(0.161) 0.941(0.143)*** 0.344(0.106)*** 0.564(0.138)*** 0.528(0.129)*** 0.764(0.180)*** 0.577(0.178)*** 1.585(0.181)*** 0.833(0.144)*** 0.033(0.088) −0.960(0.309)*** 0.848(0.243)*** 0.242(0.150) −0.150(0.101) 0.301(0.135)** −0.448(0.086)*** −0.037(0.004)*** −0.188(0.129) −0.247(0.151) −0.170(0.185) −0.410(0.250) −0.082(0.102) 0.055(0.103) 0.247(0.139)* 0.002(0.249) −0.008(0.013) −0.248(0.038)*** 0.034(0.016)** −0.820(0.258)*** 0.517(0.299)* −0.610(0.222)***

4.762(0.331)***

5.224(0.535)***

R2 Number of Observations

0.181 14,886

17,397

0.122 14,886

0.136 14,886

Notes: (1) and (2) are IV estimation with and without inverse mills ratio (Lambda) from entry into physician services to control the sample selection bias. * Significance at 10% confidence level. ** Significance at 5% confidence level. *** Significance at the 1% confidence level.

It is not surprising that the health information–trust interaction is more significant in predicting the number of visits than whether or not to visit. As stated, consumers are primary decision-makers in the decision to go at all, but once they go the decision becomes a shared one between doctor and patient. As such, we would expect to see the role of information search and the role of trust to significantly predict whether or not to go, as it does. Once a patient goes, all follow-up decisions are a function of the doctor–patient relationship, which could explain why the interaction of trust and information search becomes important. Moreover, we also find that low trusting patients who are not able to obtain useful health information from other sources may

care demand on the average, and the interaction between limited choice and information is negative in each regression, but only significant in the IV estimation for number of physician visits. It suggests that a substitution effect may also exist among patients who face limited choice in the model that allows for endogenous health information investment.

still rely on physicians as reflected by the significant positive coefficient on the variable of prior low patient trust.

6.3. Health information and number of ER visits Table 5 reports the Tobit results for ER visits. Consumer information seeking has a significant positive effect (0.128) on ER utilization. Interestingly, after addressing endogeneity using the IV approach, we find that consumer information seeking significantly reduces the number of ER visits by 1.2 times. Patient prior low trust in physician has a significant positive effect on ER visits. This finding suggests that low-trust patients, with distaste for routine physician service, may have delayed necessary care which may result in costly ER utilization. ER visits are usually emergent that does not stem from interactions with physicians. As expected, the interaction between information and patient trust is insignificant although it has a negative coefficient.

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D.S. Dwyer, H. Liu / The Quarterly Review of Economics and Finance 53 (2013) 1–11

Table 5 The effect of consumer health information on number of emergency room visits. Tobit Coef.

IV Tobit Coef.

Any Info search Prior low trust*Info Prior low trust Poor health ADL Physical limited work Emotional limited work Diabetes Arthritis Asthma Hypertension Heart disease Skin cancer Other cancers Depression HMO Uninsured Medicaid Medicare Somewhat risk-taking Strongly risk-taking Limited choice Age Male Black Hispanic Asian Married Any kid Large MSA Small MSA Family income Log wage Pop. per mile2 Physician per pop. % Female physician % Board cons

0.128(0.054)** −0.102(0.117) 0.070(0.076) 0.401(0.078)*** 0.314(0.065)*** 0.925(0.070)*** 0.022(0.086) 0.267(0.084)*** −0.012(0.066) 0.551(0.082)*** 0.230(0.063)*** 0.934(0.098)*** 0.063(0.113) 0.106(0.114) 0.390(0.081)*** 0.101(0.056)* −0.043(0.119) 0.372(0.134)*** 0.208(0.094)** 0.092(0.061) 0.507(0.077)*** −0.201(0.054)*** −0.022(0.003)*** 0.032(0.056) 0.466(0.090)*** 0.133(0.110) −0.413(0.163)** −0.271(0.063)*** 0.090(0.066) 0.002(0.088) 0.024(0.160) −0.017(0.008)** −0.106(0.024)*** 0.001(0.010) 0.063(0.162) 0.019(0.192) 0.229(0.142) −1.544(0.186)***

−1.229(0.366)*** −0.706(0.437) 0.385(0.185)** 0.394(0.079)*** 0.350(0.067)*** 1.072(0.082)*** 0.176(0.097)* 0.331(0.087)*** 0.028(0.068) 0.590(0.085)*** 0.265(0.065)*** 0.953(0.101)*** 0.156(0.118) 0.177(0.117) 0.540(0.092)*** 0.114(0.057)** −0.093(0.122) 0.272(0.140)* 0.146(0.098) 0.131(0.063)** 0.485(0.078)*** −0.228(0.056)*** −0.023(0.003)*** −0.111(0.068) 0.436(0.093)*** 0.096(0.113) −0.407(0.166)** −0.215(0.066)*** 0.043(0.068) 0.037(0.090) 0.041(0.163) −0.005(0.009) −0.089(0.025)*** 0.003(0.011) 0.202(0.168) 0.048(0.196) 0.246(0.145)* −1.183(0.212)***

Endo. test Obs

17,397

P-val = 0.00 17,397

Standard errors are in parentheses. * Significance at 10% confidence level. ** Significance at 5% confidence level. *** Significance at the 1% confidence level.

7. Conclusion and policy implications Historically the doctor–patient relationship has been characterized by a significant information asymmetry. Empowered by the information boom, patients with increasing advocacy and consumerism are now taking a more active role in the health care demand decision-making process. This study empirically investigates the determinants of patients’ consumerism behavior – health information seeking from non-physician health information sources, and the role of consumer health information in the demand for medical services. In theory consumer health information could serve as both a complement and substitute to physician services. This study aims to explore the relationship empirically. We explore submarkets that may be more likely to demonstrate some substitution between self-care through information and pricey medical services. On the demand side of the market we examine whether there are types of patients more likely to seek health information and self-care. In particular we examine the interaction of trust in the system with engagement in search for outside sources of information. We do find that for physician visits substitution is more likely to occur among information seekers with a lower propensity to trust physicians.

On the supply side of the market, we examine whether there are types of services that may be impacted differently by the information boom. In particular we study emergency room visits given the high pecuniary and non-pecuniary costs of those services. We find that information seeking does indeed reduce the need for this type of service regardless of trust in physicians. This is a significant finding given overuse in ER services and the propensity for efficiency improvements. Often individuals use the ER when physician services are not available – at night or weekends – and there is no other information source. If individuals are able to determine the need to be seen immediately through online information, they may be substituting self-care, or even next day physician services for ER services. If done well, this could reduce the burden on overwhelmed ERs and cut costs. This study does not examine health outcomes to assess quality of outcomes which is important when care is being substituted. Whether it is ER care or physician visit, it is important to examine appropriateness of the alternative care associated with information seeking if the goal is to improve efficiency. We reserve that for future work. This study motivates that type of analysis and is a first step. This paper is among the first to identify some degree of substitution of lower cost health information sources for expensive health professional advice in the general population. It suggests that health care consumerism may be an effective way to control some of the high health care costs we face and improve efficiency, at the margin. It has quite important policy implications given the cost crisis we continue to face, combined with inadequate access to medical services. Patients will continue to take an active role in their medical care, using new sources as the Internet and direct-toconsumer advertising (DTCA) for health care information continues to grow. Therefore, how the better-informed consumer impacts the health care system is an important policy concern. Although this study cannot fully evaluate the efficiency potential of the information boom, we indirectly speak to that efficiency by analyzing the relationship of consumer health information to use of services which ties to cost. A leading cause of health cost inflation is that insured individuals have incentives to indiscriminately seek more health care, rather than carefully limit expenses, shop around for second opinions, and ask questions – consumerism activities that result in more efficient and prudent expenditures of resources. Our findings suggest investments in improving access to, and the ability to decipher, quality information may prove cost-effective. We admit that patient trust in physicians has its own benefits as well as pitfalls (Davies & Rundall, 2000; Dwyer et al., 2012). Optimal trust level in physician is both a theoretical and empirical challenge. The welfare analysis on patient trust is beyond the scope of this paper. Finally, while the pervasive disparity in health care access and utilization is widely documented (Zheng & Zimmer, 2009), the causes are not sufficiently understood. Financial barriers can affect the delivery of health care, but lack of information may be another important access barrier too. For example, the social disparity in the receipt of health care among the Medicare enrollees may be reduced by providing them adequate information about the coverage and risk of illness (Parente et al., 2005). The average complement effect of consumer health information on health services suggests that the strategies to educate the public with adequate health information, especially those elderly, the poor and the minorities, may improve social welfare substantially. References Amemiya, T. (1979). The estimation of a simultaneous equation tobit model. International Economic Review, 20, 169–181.

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